1. Technical Field
The invention relates generally to biomedical methods and apparatus. More particularly, the invention relates to processing spectra to yield enhanced analyte property estimations. Still more particularly, the invention relates to generating and using an analyte filter designed to an analyte signal shape in the presence of interference, preferably in combination with multivariate analysis.
2. Description of Related Art
Digital Filtering
The technique of digitally filtering collected data is performed to isolate a portion of the data. Digital filtering is broadly broken into two types, finite impulse response (FIR) and infinite impulse response (IIR). Digital filtering techniques are used to reduce or remove background effects, to reduce or remove high frequency noise, and to enhance signal. Specific sub-types of digital filtering have been previously described to process noninvasive near-infrared data to isolate chemical or physical information related to physiology, instrumentation, and/or the environment. Specific types of digital filtering, such as derivatives and Fourier filters are discussed here.
Derivative/Convolution
A. Savitsky and M. Golay, Smoothing and differentiation of data by simplified least squares procedures, Anal. Chem., 36, 1627-40, (1964) describe a convolution based implementation of smoothing, first derivative filtering, second derivative filtering, and higher order derivative filtering for enhancing the signal-to-noise ratio of a response or a vector.
D. Haaland, M. Robinson, G. Koepp, E. Thomas, and R. Eaton, Reagentless near-infrared determination of glucose in whole blood using multivariate calibration, Appl. Spect., 46, 1575-1578, (1992) describe the use of derivatives in conjunction with glucose concentration determination. The authors suggest use of derivative spectra for the reduction of subject-to-subject or inter-subject spectral variation.
J. Hall, Method and device for measuring concentration levels of blood constituents non-invasively, U.S. Pat. No. 5,361,758 (Nov. 8, 1994) describes the use of a derivative in processing of near-infrared noninvasive spectra. J. Samsoondar, Method for calibrating spectrophotometric apparatus with synthetic fluids to measure plasma and serum analytes, U.S. Pat. No. 6,470,279 (Oct. 22, 2002); J. Samsoondar, Method for calibrating spectrophotometric apparatus, U.S. Pat. No. 6,611,777 (Aug. 6, 2003); and J. Samsoondar, Method for calibrating spectrophotometric apparatus, U.S. Pat. No. 6,651,015; Nov. 18, 2003) describe the use of order derivatives to process spectra. The processed spectra are used on spectra collected on a second instrument to enhance calibration transfer developed on at least one additional instrument. In this document, an order derivative is also referred to as a mathematical derivative.
Fourier Filtering
M. Arnold, et. al., Determination of physiological levels of glucose in an aqueous matrix with digitally filtered Fourier transformed near-infrared spectra, Anal. Chem., 62, 1457-1464, (1990); G. Small, et. al. Strategies for Coupling Digital Filtering with partial least-squares regression: application to the determination of glucose in plasma by Fourier transform near-infrared spectroscopy, Anal. Chem, 65, 3279-3289, (1993); G. Small, M. Arnold Method and apparatus for non-invasive detection of physiological chemicals, particularly glucose, U.S. Pat. No. 5,459,317 (Oct. 17, 1995); and G. Small, M. Arnold, Method and apparatus for non-invasive detection of physiological chemicals, particularly glucose, U.S. Pat. No. 6,061,582 (May 9, 2000) describe the use of a Gaussian digital filter for processing absorbance data after transforming from a wavelength to a frequency domain.
Calibration Transfer
Overview
Calibration transfer is a standardization procedure designed to eliminate a full recalibration and to maintain information residing in the existing model. Calibration transfer is useful because sources of variation in the instrument and environment are modeled in the development of a training or calibration set. Therefore, as the instrument or environment state changes the model components does not exactly match the current state.
Identical performance of analytical instruments is unrealistic even with the successful implementation of tight quality control on instrument hardware. For example, variation in the output of a source, quality of lenses or mirrors, alignment, and detector response, which are limited by manufacturing tolerances, result in differences between spectrometers even of the same design. The instrument differences result in spectra of the master instrument varying from that of the slave instrument. Variations between the spectrometers result in errors when using a calibration developed on a spectrometer to determine parameters with a second spectrometer. Generally, this error is increasingly detrimental as the signal-to-noise ratio of the determined analyte decreases. Several techniques for calibration transfer are presented here.
Robustness
One approach to calibration transfer is to generate a robust model that covers all future conditions. Experimental design is used to develop a robust calibration. For noninvasive glucose concentration determinations, parameters include measurement conditions, such as temperature and humidity, as well as analyte/constituent concentration distributions. This approach is effective in controlled environments when the analyte signal-to-noise ratio is strong. However, the technique is not efficient in terms of time and money. Also, the quality of the calibration is suspect in terms of inability to predict future conditions that need to be incorporated into the original calibration. In addition, the technique does not readily allow incorporation of future conditions that are later identified without a new experimental design and development of a new or updated calibration.
Full Recalibration
Full recalibration of an analyzer is not preferable due to time requirements, technical expertise requirements, and expense. In addition, recalibration often fails to capture a full range of parameters, such as variations in the environment and instrument, thereby forcing additional recalibrations as the state of these parameters change.
Axis Standardization
Calibration transfer is used to compensate for changes to an axis, such as an x-axis or a y-axis. For spectrophotometric based determination, an approach to x-axis stability is to provide, with each sample or on a daily basis, a spectrum of a standard that is used to determine the x-axis. For adjustment of the x-axis in the near-infrared, polystyrene is often used. Additional near-infrared wavelength standards include rare earth oxides, such as holmium oxide, erbium oxide, and dysprosium oxide. Each standard or reference provides multiple peaks that are used to set or adjust the x-axis, such as a wavelength axis. In its broadest sense, any material that yields known or reproducible peaks for a given state is usable as an x-axis standard.
Calibration transfer is also used to adjust or compensate for changes to a y-axis. For example, a y-axis is commonly adjusted with a reference standard. Examples of diffuse reflectance standards in the near-infrared include polytetrafluoroethylene diffuse reflectance standards, such as diffuse reflectance standards that come with diffuse reflectances of 2, 5, 10, 20, 40, 60, 80, and 99%.
Another approach is the use of standards that simulate the target sample, such as a tissue phantom or intralipid. In its broadest sense, any material that yields known or reproducible transmittance, reflectance, or diffuse reflectance is usable as a y-axis standard.
A number of difficulties exist for remeasuring standards. First, instability of the sample creates difficulties in producing a spectrum that is constant across time. Second, reproducing the environment, which affects the resulting spectra is difficult. For example, temperature and humidity effect spectra. Third, movement of the analyzer is an issue due to alignment. For example, this is relevant when the analyzer is moved from a lab to a process line or from a laboratory or production facility to a hospital or home setting. Replacement of analyzer components also leads to generation of spectra that are not reproducible.
Diabetes
Diabetes is a chronic disease that results in improper production and use of insulin, a hormone that facilitates glucose uptake into cells. While a precise cause of diabetes is unknown, genetic factors, environmental factors, and obesity appear to play roles. Diabetics have increased risk in three broad categories: cardiovascular heart disease, retinopathy, and neuropathy. Complications of diabetes include: heart disease and stroke, high blood pressure, kidney disease, neuropathy (nerve disease and amputations), retinopathy, diabetic ketoacidosis, skin conditions, gum disease, impotence, and fetal complications. Diabetes is a leading cause of death and disability worldwide. Moreover, diabetes is merely one among a group of disorders of glucose metabolism that also includes impaired glucose tolerance and hyperinsulinemia, which is also referred to as hypoglycemia. It continues to be beneficial to have increased accuracy and precision of estimation of glucose concentration from noninvasive spectra.
There remains an unsolved need for extracting data from spectral data, such as noninvasive spectra, that is useful in generating subsequent analyte property estimations. It would be advantageous to provide a method and apparatus for enhancing the analysis of noninvasive spectra, resulting in improved analytical performance within an instrument, across instruments, and across states using an analytical filter.